As part of the GOES-R Risk Reduction Program, a Lagrangian NearCasting approach has been developed that optimizes the impact and retention of information provided by satellites, specifically detecting and preserving intense vertical and horizontal variations observed in the various derived products over time. Results using both GOES and MSG-SEVERI data showed that the system captures and retains details (maxima, minima and extreme gradients) important to predicting the development of vertical moisture structures critical for determining the timing/location/intensity of convection 3-6 hours in advance, even after the IR observations may no longer be available due to obscuration by the developing cloud shields.
Developmental tests and real-time evaluations have demonstrated that NearCast products using full-resolution SEVERI or GOES data can be exceptionally useful in predicting isolated severe convective events not captured in convection NWP guidance. However, display tools still need to be refined so that users can rapidly take full advantage of the perishable data sets. These include: 1) Improvements in display capabilities to incorporate multiple parameters in single displays for improved forecaster understanding, 2) Combinations of multiple stability parameters and mergers with measures of forecast confidence/consistency, and 3) Looping capabilities that combine the benefits of both grid-point (Eulerian) and trajectory (Lagrangian) displays. Because of the desire to reduce false alarms and increase probability of detection, both destabilization and stabilization are studied. Examples will include cases of severe convection over the US and Europe using temperature and moisture data.